The overall framework is shown in Figure REF . The reference encoder \(E_r\)  first encodes \(R_s\)  into \(k\) -shot reference maps \(F_r = \left\lbrace f_{i}\right\rbrace _{i=1}^k\) , where \(f_i\)  is encoded from \(y_i\) . The content encoder \(E_c\)  extracts the content feature map \(f_c\)  from the input content image \(x_c\) . Our proposed SAM takes \(F_r\)  and \(f_c\)  as inputs, attends to the corresponding spatially local styles in the reference maps \(F_r\)  and aggregates the local styles into the target style map \(f_{c, r} = SAM(f_c, F_r)\) . In the end, the decoder \(D\)  decodes \(f_{c, r}\)  into the generated output image \(\hat{y}_{c}\) . A multi-task projection discriminator[1]} is employed to discriminate each generated image and real image. The discriminator outputs a binary classification of fake or real for each character's style and content category.
